NB-FOXR2 project [source]
04 - QC and data preparation for single-cell tumor data
Selin Jessa [selin.jessa@mail.mcgill.ca] and Bhavyaa Chandarana [bhavyaa.chandarana@mail.mcgill.ca]
01 November, 2024
1 Configuration
Configuration of project directory & analysis outputs:
Show full config
source(here("rr_helpers.R"))
# Set up outputs
message("Document index: ", doc_id)## Document index: 04# Specify where to save outputs
out <- here("output", doc_id); dir.create(out, recursive = TRUE)
figout <- here("figures", doc_id, "/")
cache <- paste0("/project/kleinman/bhavyaa.chandarana/cache/NB-FOXR2/public/", doc_id, "/")The root directory of this project is:
## /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/publicOutputs and figures will be saved at these paths, relative to project root:
## public/output/04## public/figures/04//Setting a random seed:
set.seed(100)2 Overview
Data for single-cell tumor samples (sc/snRNAseq, scMultiome) have been preprocessed using our in-house workflow based on Seurat/Signac, which loads Cellranger output, performs QC to filter cells, and applies normalization, dimensionality reduction, and clustering. These are run under data/singlecell/pipeline_sc* (except pipeline_scMultiome_mm which is for processing of mouse samples).
For each sample, inferCNV was also applied, to infer normal and malignant cells based on the single-cell data. This was run inside each sample's pipeline directory under data/singlecell/pipeline_sc*/<sample>/inferCNV. Malignant and normal cells were called individually for each sample in the file data/singlecell/pipeline_sc*/<sample>/inferCNV/window101_exp0.1_refG34normcleaned_HMMnone/infer_cnv_call.html.
Following the preprocessing, all samples were merged into one object and normalization/dimensionality reduction/clustering re-computed. This was performed at data/singlecell/integrations. No correction for batch/sample/technology differences were used.
In this document, we load outputs from these preprocessing steps in the form of Seurat objects. We will perform post-cluster QC (removing any low quality clusters), explore the merged dataset, and assign normal and malignant cells based on both the inferCNV outputs, and the co-clustering of cells from different samples in the merged object.
3 Libraries
library(here)
library(magrittr)
library(tidyr)
library(dplyr)
library(readr)
library(stringr)
library(readxl)
library(glue)
library(purrr)
library(ggplot2)
library(cowplot)
library(Seurat)
library(Signac)
source(here("include/style.R"))
source(here("code/functions/scRNAseq.R"))
source(here("code/functions/ssGSEA.R"))
ggplot2::theme_set(theme_min())
square_theme <- theme(aspect.ratio = 1)
large_text <- theme(text = element_text(size = 15))4 Load metadata
meta_sc <- read_tsv(here("output/00/metadata_patients_sc.tsv"))## Rows: 16 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (23): Sample, ID_Patient, ID_Sample, ID, FOXR2_positive, Group, Source, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.sc_samples_foxr2 <- meta_sc %>% filter(Group == "NB-FOXR2") %>% pull(ID)
palette_sample <- read_tsv(here("output/00/sc_info.samples.tsv")) %>%
select(Sample, Color) %>% tibble::deframe()## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): Path, Sample, Technology, Group, Color
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5 Post-cluster QC
Load the seurat objects, as produced by the preprocessing workflows.
samples_sc <- map(sc_samples_foxr2, ~ get(load(meta_sc[meta_sc$ID == .x, ]$Path)))
names(samples_sc) <- sc_samples_foxr2# create a function to plot the violin plots for each QC stat
plot_vln_stat_per_cluster <- function(stat, lims = c(0, NA), labels = FALSE, seurat) {
# get the requested statistic
df <- seurat@meta.data
df$Stat <- df[[stat]]
# ggplot violin plot
p1 <- df %>% ggplot(aes(x = seurat_clusters, y = Stat)) +
geom_violin(aes(fill = seurat_clusters), scale = "width") +
scale_fill_manual(values = seurat@misc$colours) +
rotate_x() + no_legend() +
# zoom in on y-axis if needed,
# without changing distribution
# and flip x/y axes
coord_flip(ylim = lims) +
xlab(NULL) +
ggtitle(stat)
# remove sample labels if needed
# (after coord flip, samples are on the y-axis)
if (!labels) p1 <- p1 + theme(axis.text.y = element_blank()) + ylab(NULL)
p1
}
# wrapper function to plot all QC violin plots for one sample, given the seurat object
plot_qc_per_sample <- function(seurat, nCount_max = 20000, nFeature_max = 7000) {
stats <- replicate(5, c(0, NA), simplify = FALSE)
names(stats) <- c("nCount_RNA",
"nFeature_RNA",
"percent.mito",
"percent.ribo",
"G2M.Score")
stats$nCount_RNA <- c(0, nCount_max)
stats$nFeature_RNA <- c(0, nFeature_max)
stats$G2M.Score <- c(-0.5, 1.5)
# map over:
# 1. stat
# 2. y limits
# 3. whether to keep labels or not
pmap(list(names(stats), stats, c(TRUE, rep(FALSE, 4))),
~ plot_vln_stat_per_cluster(..1, ..2, ..3, seurat)) %>%
{plot_grid(plotlist = ., nrow = 1, align = "h", axis = "tb",
rel_widths = c(0.25, rep(0.18, 4)))}
}Plot per-cluster QC in each sample (separated into tabs).
5.1 P-2236_S-2236
plot_qc_per_sample(samples_sc$`P-2236_S-2236`)
In this sample, we flag cluster 7 as low-QC for downstream analysis.
5.2 P-2273_S-2273
plot_qc_per_sample(samples_sc$`P-2273_S-2273`, nCount_max = 8000, nFeature_max = 5000)
5.3 P-6776_S-10153
plot_qc_per_sample(samples_sc$`P-6776_S-10153`)
5.4 P-6777_S-10154
plot_qc_per_sample(samples_sc$`P-6777_S-10154`)
5.5 P-6778_S-10155
plot_qc_per_sample(samples_sc$`P-6778_S-10155`, nCount_max = 25000, nFeature_max = 7000)
5.6 NBFOXR2_6
plot_qc_per_sample(samples_sc$`NBFOXR2_6`, nCount_max = 10000, nFeature_max = 4000)
6 Naive-merged dataset
6.1 Load data
Load integrated data (without batch correction or harmonization):
load(here("data/singlecell/integrations/NB-FOXR2_naive_join/output/seurat_joint.Rda"))
seurat_joint$Joint_cluster <- Idents(seurat_joint)6.2 Overview
Plot an overview of the merged dataset:
seurat_joint@meta.data <- seurat_joint@meta.data %>%
mutate(Technology = case_when(Sample == "NBFOXR2_6" ~ "10X Single Cell 3'",
TRUE ~ as.character(Technology)))
plot_grid(
umap(seurat_joint, color_by = "Technology", alpha = 0.5, label = FALSE, point_size = 0.5) +
mod_legend_col() + square_theme + large_text,
umap(seurat_joint, color_by = "Sample", colors = palette_sample, alpha = 0.5, label = FALSE, point_size = 0.5) +
mod_legend_col() + square_theme + large_text,
umap(seurat_joint, legend = TRUE, alpha = 0.5, point_size = 0.5) +
mod_legend_col() + square_theme + large_text,
align = "h", axis = "tb", nrow = 1, rel_widths = c(0.35, 0.33, 0.30))
Produce separate rasterized plots for publication.
umap(seurat_joint, color_by = "Technology", alpha = 0.5, label = FALSE, point_size = 1,
rasterize = T) +
mod_legend_col() +
theme(text=element_text(size=30),
legend.position = "bottom") +
square_theme
umap(seurat_joint, color_by = "Sample", colors = palette_sample, alpha = 0.95, label = FALSE, point_size = 1,
rasterize = T) +
mod_legend_col() +
theme(text=element_text(size=31),
legend.position = "bottom") +
square_theme
umap(seurat_joint, legend = TRUE, alpha = 0.5, point_size = 1,
rasterize = T) +
no_legend() +
theme(text=element_text(size=31)) +
square_theme
6.3 QC of merged data
Plot QC metrics in the merged dataset.
sc_meta <- seurat_joint@meta.data
# harmonize column names for scRNA/Multiome preprocessing
qc_rna <- sc_meta %>%
filter(Technology == "10X Single Nuclei 3'") %>%
select(Sample, Technology, nCount_RNA, nFeature_RNA, percent.mito, percent.ribo, G2M.Score, Harmony_cluster = seurat_clusters)
qc_multi <- sc_meta %>%
filter(Technology == "10X Multiome") %>%
select(Sample, Technology, nCount_RNA, nFeature_RNA, percent.mito, percent.ribo, G2M.Score,
nCount_peaks, nFeature_peaks, atac_peak_region_fragments, nucleosome_signal, TSS.enrichment, Harmony_cluster = seurat_clusters)
all_multi <- bind_rows(qc_rna, qc_multi) %>%
arrange(Technology) %>%
mutate(Sample = factor(Sample, levels = unique(.$Sample))) %>%
mutate(Harmony_cluster = factor(Harmony_cluster, levels = as.character(sort(as.numeric(levels(qc_multi$Harmony_cluster))))))
# create a function to plot the violin plots for each QC stat
plot_vln_stat_per_cluster <- function(stat, lims = c(0, NA), labels = FALSE) {
# get the requested statistic
df <- all_multi
df$Stat <- df[[stat]]
# ggplot violin plot
p1 <- df %>% ggplot(aes(x = Harmony_cluster, y = Stat)) +
geom_violin(aes(fill = Harmony_cluster), scale = "width") +
# scale_fill_manual(values = palette_sample) +
rotate_x() + no_legend() +
# zoom in on y-axis if needed,
# without changing distribution
# and flip x/y axes
coord_flip(ylim = lims) +
xlab(NULL) +
ggtitle(stat)
# remove sample labels if needed
# (after coord flip, samples are on the y-axis)
if (!labels) p1 <- p1 + theme(axis.text.y = element_blank()) + ylab(NULL)
p1 + large_text
}
stats <- replicate(5, c(0, NA), simplify = FALSE)
names(stats) <- c("nCount_RNA",
"nFeature_RNA",
"percent.mito",
"percent.ribo",
"G2M.Score")
stats$nCount_RNA <- c(0, 25000)
stats$nFeature_RNA <- c(0, 7500)
stats$G2M.Score <- c(-0.5, 1.5)
# number of cells in each cluster
p1 <- sc_meta %>%
ggplot(aes(x = seurat_clusters)) +
geom_bar(aes(fill = seurat_clusters)) +
coord_flip() +
no_legend() +
ggtitle("# cells") +
large_text
p2 <- sc_meta %>%
ggplot(aes(x = seurat_clusters)) +
geom_bar(aes(fill = Sample), position = "fill") +
scale_fill_manual(values = palette_sample) +
theme(panel.border = element_blank()) +
rotate_x() +
ggtitle("Cluster breakdown by sample") +
coord_flip() +
no_legend() +
large_text
# map over:
# 1. stat
# 2. y limits
# 3. whether to keep labels or not
vln_plots <- pmap(list(names(stats[c("nCount_RNA", "nFeature_RNA", "percent.mito", "percent.ribo", "G2M.Score")]),
stats[c("nCount_RNA", "nFeature_RNA", "percent.mito", "percent.ribo", "G2M.Score")],
rep(FALSE, 5)), ~ plot_vln_stat_per_cluster(..1, ..2, ..3))
plot_grid(plotlist = c(list(p1, p2), vln_plots),
nrow = 1, align = "h", axis = "tb", rel_widths = c(0.2, 0.2, rep(0.15, 5)))
plot_grid(
umap(seurat_joint, "nCount_RNA", color_by_type = "continuous", colors = rdbu, alpha = 0.5, label = FALSE, point_size = 0.5) + square_theme + large_text,
umap(seurat_joint, "nFeature_RNA", color_by_type = "continuous", colors = rdbu, alpha = 0.5, label = FALSE, point_size = 0.5) + square_theme + large_text,
umap(seurat_joint, "percent.mito", color_by_type = "continuous", colors = rdbu, alpha = 0.5, label = FALSE, point_size = 0.5) + square_theme + large_text,
umap(seurat_joint, "percent.ribo", color_by_type = "continuous", colors = rdbu, alpha = 0.5, label = FALSE, point_size = 0.5) + square_theme + large_text,
align = "hv", axis = "rltb")
6.4 Normal vs. malignant calling
If a merged cluster contains >5% Normal cells called by inferCNV, we will label the whole cluster as Normal.
# get the proportion in each cluster assigned as normal or malignant
prop_category <- prop.table(table(Idents(seurat_joint), seurat_joint$inferCNV), margin = 1)
prop_category##
## Malignant Normal
## 0 0.9993148338 0.0006851662
## 1 0.9995025494 0.0004974506
## 2 1.0000000000 0.0000000000
## 3 0.9992619926 0.0007380074
## 4 1.0000000000 0.0000000000
## 5 1.0000000000 0.0000000000
## 6 0.9993442623 0.0006557377
## 7 0.9975649351 0.0024350649
## 8 1.0000000000 0.0000000000
## 9 0.5885608856 0.4114391144
## 10 1.0000000000 0.0000000000
## 11 0.6567567568 0.3432432432
## 12 0.9964664311 0.0035335689
## 13 0.9215686275 0.0784313725
## 14 0.9932432432 0.0067567568
## 15 0.6521739130 0.3478260870# if more than 5% of cells in one cluster are called Normal, call the cells
# in the cluster which weren't as Normal
seurat_joint$Malignant_normal <- map2_chr(
Idents(seurat_joint), seurat_joint$inferCNV,
function(cluster, infercnv) {
if (infercnv == "Malignant" & prop_category[cluster, "Normal"] > 0.05) "Normal"
else infercnv
})
table(seurat_joint$Malignant_normal)##
## Malignant Normal
## 33516 1204merged_sc_meta <- seurat_joint@meta.data
save(merged_sc_meta, file = glue("{out}/merged_sc_meta.Rda"))Plot the malignant vs. normal cell labels.
umap(seurat_joint,
color_by = "Malignant_normal",
colors = c("Normal" = "blue",
"Malignant" = "pink"),
rasterize = TRUE,
point_size = 1,
label = FALSE,
legend = T,
alpha = 0.5,
title = glue("Malignant/normal"),
hide_axes = T) +
mod_legend_col() +
theme(text=element_text(size=30),
legend.position = "bottom") + square_theme
map(c("Malignant", "Normal"),
function(i) {
# https://github.com/satijalab/seurat/issues/3666#issuecomment-721307360
cells <- names(which(seurat_joint$Malignant_normal == i))
umap(seurat_joint,
color_by = "Malignant_normal",
colors = "#000000",
cells = cells,
rasterize = TRUE,
point_size = 0.5,
# put cells not selected in grey
na_color = "grey80",
label = FALSE,
legend = FALSE,
title = glue("{i} - {length(cells)} cells"),
hide_axes = T) +
theme(text=element_text(size=30))
}) %>%
{cowplot::plot_grid(plotlist = ., nrow = 1)}
7 Infer NBFOXR2_6 patient sex
Metadata for patient sample NBFOXR2_6 did not include patient sex. Here, we will infer the biological sex of this patient based on expression of sex-specific genetic markers compared with the other five single-cell samples which include patient sex in metadata:
2236- M2273- F10153- F10154- M10155- F
We will plot the following sex specific genes across single-cell samples:
- XIST (X chromosome)
- DDX3Y, ZFY, UTY, USP9Y (Y chromosome)
(Publication: Olney et al. Biology of Sex Differences 2020)
VlnPlot(object = seurat_joint,
features = c("XIST", "DDX3Y", "ZFY", "UTY", "USP9Y"), cols = palette_sample,
group.by = "Sample",pt.size = 0)
Patient sample NBFOXR2_6 more closely matches gene expression of patient samples assigned male in the metadata. Therefore we also assign this patient male in our metadata.
Based on this information, our single-cell cohort for NB-FOXR2 is balanced for patient sex.
8 TABLE: Human single-cell QC
Export a supplementary table for human single-cell RNA-seq QC.
meta_sc_foxr2 <- meta_sc %>% filter(Group == "NB-FOXR2") %>%
mutate(scRNAseq_path = gsub("data/", "", scRNAseq_path),
Note = case_when(
scRNAseq_path != "-" ~ "GEX from scRNAseq",
scMultiome_path != "-" & ID != "P-6778_S-10155" ~ "Only GEX used from scMultiome",
ID == "P-6778_S-10155" ~ "GEX and ATAC from scMultiome"
))
meta_sc_foxr2_scRNA_only <- meta_sc_foxr2 %>% filter(ID != "P-6778_S-10155")
# get sample prep info ---------------------------------------------------------
# get paths and IDs:
path_id_map <- meta_sc_foxr2 %>%
select(ID, scRNAseq_path, scMultiome_path) %>%
mutate(Path = ifelse(scRNAseq_path != "-", scRNAseq_path, scMultiome_path)) %>%
select(ID, Path)
scRNA_omega <- read_xlsx(here("data/metadata/20240404-Omegatable-singlecell.xlsx")) %>%
select(Sample,
Path,
Protocol,
Publication,
Kit = `kit version`,
Method_of_dissociation = `Method of dissociation`,
Starting_material = `Starting material`,
Starting_material_amount = `tissue (mg)`,
Targeted_N_cells = `cell/nuclei target`,
Sequencing_platform = `Instrument`) %>%
filter(Path %in% meta_sc_foxr2_scRNA_only$scRNAseq_path | Path %in% meta_sc_foxr2_scRNA_only$scMultiome_path) %>%
left_join(path_id_map, by = "Path")## New names:
## • `library yield (nM)` -> `library yield (nM)...36`
## • `copies/ul` -> `copies/ul...37`
## • `copies/ul` -> `copies/ul...38`
## • `library yield (nM)` -> `library yield (nM)...39`# get bioinformatics QC --------------------------------------------------------
scRNAseq_qc <- map2_dfr(meta_sc_foxr2_scRNA_only$ID, meta_sc_foxr2_scRNA_only$sc_preprocessing_path_hydra,
~ data.table::fread(file.path(.y, "seurat_metrics.tsv"), data.table = FALSE) %>%
tibble::add_column(ID = .x, ID_omega = .y, .before = 1)) %>%
left_join(scRNA_omega, by = "ID")
# get Cellranger QC ------------------------------------------------------------
scRNA_cellranger_qc <- map2_dfr(meta_sc_foxr2_scRNA_only$ID, meta_sc_foxr2_scRNA_only$Cellranger_path,
~ data.table::fread(.y, data.table = FALSE) %>%
mutate_all(as.character) %>%
tibble::add_column(ID = .x, .before = 1) %>%
tibble::add_column(Cellranger_path = .y, .after = 1))
# # get processing params --------------------------------------------------------
scRNA_config_params <- imap_dfr(samples_sc[meta_sc_foxr2_scRNA_only$ID], ~ .x@misc$params$config %>%
# drop the parameter which indicates the genes plot during preprocessing
discard_at("genes") %>%
data.frame() %>%
mutate(ID = .y))
scRNA_config_params2 <- scRNA_config_params %>%
select(ID,
Min_cells = min_cells,
Min_features = min_features,
Vars_to_regress = var_regress,
N_principal_components = pcs_keep,
Clustering_resolution = clustering_resolution,
Seed = seed)
TABLE_scRNAseq_qc <- scRNAseq_qc %>%
left_join(scRNA_cellranger_qc, by = c("ID")) %>%
left_join(scRNA_config_params2, by = "ID") %>%
left_join(meta_sc_foxr2_scRNA_only %>% select(ID, Note)) %>%
select(
# Sample info
ID,
# QC
Note,
Protocol,
Sequencing_platform,
Starting_material_amount,
Method_of_dissociation,
Targeted_N_cells,
N_reads = `Number of Reads`,
N_cells_estimated = `Estimated Number of Cells`,
N_cells_after_filtering = N_cells_after,
Reads_mapped_to_genome = `Reads Mapped to Genome`,
Reads_mapped_to_transcriptome = `Reads Mapped Confidently to Transcriptome`,
Min_cells, Min_features, Vars_to_regress, N_principal_components, Clustering_resolution, Seed,
Mean_mitochondrial_content_after_filtering = percent.mito_mean.postQC,
Mean_UMIs_after_filtering = nCount_RNA_mean.postQC,
Mean_N_genes_after_filtering = nFeature_RNA_mean.postQC,
Max_mito_threshold = percent.mito_max.threshold,
Min_N_genes_threshold = nFeature_RNA_min.threshold,
Max_N_genes_threshold = nFeature_RNA_max.threshold,
Max_UMIs_threshold = nCount_RNA_max.threshold) %>%
arrange(ID)## Joining with `by = join_by(ID)`rr_write_tsv(TABLE_scRNAseq_qc,
glue("{out}/TABLE_scRNAseq_QC.tsv"),
"Summary of sample info and QC for scRNAseq of human tumor samples")## ...writing description of TABLE_scRNAseq_QC.tsv to public/output/04/TABLE_scRNAseq_QC.descCreate a similar table for the multiome sample:
meta_sc_foxr2_Multiome_only <- meta_sc_foxr2 %>% filter(ID == "P-6778_S-10155") %>%
mutate(Cellranger_path = file.path("/project/kleinman/singlecell_pipeline/data/",
scMultiome_path,
"cellranger-arc_count/summary.csv"))
# get sample prep info ---------------------------------------------------------
multiome_omega <- read_xlsx(here("data/metadata/20240404-Omegatable-singlecell.xlsx")) %>%
select(Sample,
Path,
Protocol,
Publication,
Kit = `kit version`,
Method_of_dissociation = `Method of dissociation`,
Starting_material = `Starting material`,
Starting_material_amount = `tissue (mg)`,
Targeted_N_cells = `cell/nuclei target`,
Sequencing_platform = `Instrument`) %>%
filter(Path %in% meta_sc_foxr2_Multiome_only$scMultiome_path) %>%
left_join(path_id_map, by = "Path")## New names:
## • `library yield (nM)` -> `library yield (nM)...36`
## • `copies/ul` -> `copies/ul...37`
## • `copies/ul` -> `copies/ul...38`
## • `library yield (nM)` -> `library yield (nM)...39`# get bioinformatics QC --------------------------------------------------------
multiome_qc <- data.table::fread(file.path(meta_sc_foxr2_Multiome_only$sc_preprocessing_path_hydra, "seurat_metrics.tsv"), data.table = FALSE) %>%
tibble::add_column(ID = meta_sc_foxr2_Multiome_only$ID,
ID_omega = meta_sc_foxr2_Multiome_only$sc_preprocessing_path_hydra,
.before = 1) %>%
left_join(multiome_omega, by = "ID")
# get Cellranger QC ------------------------------------------------------------
multiome_cellranger_qc <- data.table::fread(meta_sc_foxr2_Multiome_only$Cellranger_path, data.table = FALSE) %>%
mutate_all(as.character) %>%
tibble::add_column(ID = meta_sc_foxr2_Multiome_only$ID, .before = 1) %>%
tibble::add_column(Cellranger_path = meta_sc_foxr2_Multiome_only$Cellranger_path, .after = 1)
# # get processing params --------------------------------------------------------
multiome_config_params <- samples_sc$`P-6778_S-10155`@misc$params$config %>%
discard_at("genes") %>%
data.frame() %>%
mutate(ID = "P-6778_S-10155")
multiome_config_params2 <- multiome_config_params %>%
select(ID,
Min_cells = min_cells,
N_principal_components_RNA = rna_pcs_keep,
N_principal_components_ATAC = atac_pcs_keep,
Clustering_resolution = clustering_resolution,
Seed = seed)
TABLE_multiome_qc <- multiome_qc %>%
left_join(multiome_cellranger_qc, by = c("ID")) %>%
left_join(multiome_config_params2, by = "ID") %>%
select(
# Sample info
ID,
# QC
Protocol,
Sequencing_platform,
Starting_material_amount,
Method_of_dissociation,
Targeted_N_cells,
Genome,
Cellranger_version = `Pipeline version`,
N_cells_estimated = `Estimated number of cells`,
N_cells_after_filtering = N_cells_after,
GEX_Reads_mapped_to_genome = `GEX Reads mapped confidently to genome`,
GEX_Reads_mapped_to_transcriptome = `GEX Reads mapped confidently to transcriptome`,
GEX_Mean_mitochondrial_content_after_filtering = percent.mito_mean.postQC,
GEX_Mean_UMIs_after_filtering = nCount_RNA_mean.postQC,
GEX_Mean_N_genes_after_filtering = nFeature_RNA_mean.postQC,
GEX_Max_mito_threshold = percent.mito_max.threshold,
GEX_Min_N_genes_threshold = nFeature_RNA_min.threshold,
GEX_Max_N_genes_threshold = nFeature_RNA_max.threshold,
GEX_Min_UMIs_threshold = nCount_RNA_min.threshold,
GEX_Max_UMIs_threshold = nCount_RNA_max.threshold,
ATAC_Fraction_transposition_events_in_peaks_in_cells = `ATAC Fraction of transposition events in peaks in cells`,
ATAC_Fraction_mapped_confidently = `ATAC Confidently mapped read pairs`,
ATAC_Fraction_fragments_in_peaks = `ATAC Fraction of high-quality fragments overlapping peaks`,
Min_cells, Clustering_resolution, N_principal_components_RNA, N_principal_components_ATAC, Seed,
ATAC_Median_fragments_per_cell = `ATAC Median high-quality fragments per cell`,
ATAC_Mean_nucleosome_signal_after_filtering = nucleosome_signal_mean.postQC,
ATAC_Mean_peak_region_fragments_after_filtering = atac_peak_region_fragments_mean.postQC,
ATAC_Mean_TSS_enrichment_after_filtering = TSS.enrichment_mean.postQC,
ATAC_Min_peak_region_fragments_threshold = atac_peak_region_fragments_min.threshold,
ATAC_Max_peak_region_fragments_threshold = atac_peak_region_fragments_max.threshold,
ATAC_Max_nucleosome_signal_threshold = nucleosome_signal_max.threshold,
ATAC_Min_TSS_enrichment_threshold = TSS.enrichment_min.threshold,
ATAC_Max_TSS_enrichment_threshold = TSS.enrichment_max.threshold)
rr_write_tsv(TABLE_multiome_qc,
glue("{out}/TABLE_scMultiome_QC.tsv"),
"Summary of sample info and QC for scMultiome of human tumor sample (n=1)")## ...writing description of TABLE_scMultiome_QC.tsv to public/output/04/TABLE_scMultiome_QC.desc9 Reproducibility
This document was last rendered on:
## 2024-11-01 11:51:38The git repository and last commit:
## Local: main /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/public
## Remote: main @ origin (https://github.com/fungenomics/NB-FOXR2.git)
## Head: [0e89693] 2024-09-12: Initial commitThe random seed was set with set.seed(100)
The R session info:
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os Rocky Linux 8.10 (Green Obsidian)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Toronto
## date 2024-11-01
## pandoc 1.19.2.1 @ /cvmfs/soft.computecanada.ca/gentoo/2020/usr/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date (UTC) lib source
## P abind 1.4-5 2016-07-21 [?] CRAN (R 4.1.2)
## P beeswarm 0.4.0 2021-06-01 [?] CRAN (R 4.1.2)
## P BiocGenerics 0.40.0 2021-10-26 [?] Bioconductor
## P BiocManager 1.30.15 2021-05-11 [?] CRAN (R 4.1.2)
## P BiocParallel 1.28.3 2021-12-09 [?] Bioconductor
## P Biostrings 2.62.0 2021-10-26 [?] Bioconductor
## P bit 4.0.4 2020-08-04 [?] CRAN (R 4.1.2)
## P bit64 4.0.5 2020-08-30 [?] CRAN (R 4.1.2)
## P bitops 1.0-7 2021-04-24 [?] CRAN (R 4.1.2)
## P bslib 0.3.1 2021-10-06 [?] CRAN (R 4.1.2)
## P cachem 1.0.6 2021-08-19 [?] CRAN (R 4.1.2)
## P Cairo 1.6-0 2022-07-05 [?] CRAN (R 4.1.2)
## P callr 3.7.6 2024-03-25 [?] RSPM
## P cellranger 1.1.0 2016-07-27 [?] CRAN (R 4.1.2)
## P cli 3.6.1 2023-03-23 [?] RSPM (R 4.1.2)
## P cluster 2.1.2 2021-04-17 [?] CRAN (R 4.1.2)
## P codetools 0.2-18 2020-11-04 [?] CRAN (R 4.1.2)
## P colorspace 2.0-2 2021-06-24 [?] CRAN (R 4.1.2)
## P cowplot * 1.1.1 2020-12-30 [?] CRAN (R 4.1.2)
## P crayon 1.4.2 2021-10-29 [?] CRAN (R 4.1.2)
## P data.table 1.14.2 2021-09-27 [?] CRAN (R 4.1.2)
## P deldir 1.0-6 2021-10-23 [?] CRAN (R 4.1.2)
## P devtools 2.4.5 2022-10-11 [?] CRAN (R 4.1.2)
## P digest 0.6.35 2024-03-11 [?] CRAN (R 4.1.2)
## P docopt 0.7.1 2020-06-24 [?] CRAN (R 4.1.2)
## P dplyr * 1.1.1 2023-03-22 [?] CRAN (R 4.1.2)
## P ellipsis 0.3.2 2021-04-29 [?] CRAN (R 4.1.2)
## P evaluate 0.23 2023-11-01 [?] CRAN (R 4.1.2)
## P fansi 1.0.2 2022-01-14 [?] CRAN (R 4.1.2)
## P farver 2.1.0 2021-02-28 [?] CRAN (R 4.1.2)
## P fastmap 1.1.0 2021-01-25 [?] CRAN (R 4.1.2)
## P fastmatch 1.1-3 2021-07-23 [?] CRAN (R 4.1.2)
## P fitdistrplus 1.1-6 2021-09-28 [?] CRAN (R 4.1.2)
## P fs 1.5.2 2021-12-08 [?] CRAN (R 4.1.2)
## P future 1.25.0 2022-04-24 [?] CRAN (R 4.1.2)
## P future.apply 1.8.1 2021-08-10 [?] CRAN (R 4.1.2)
## P generics 0.1.3 2022-07-05 [?] CRAN (R 4.1.2)
## P GenomeInfoDb 1.30.1 2022-01-30 [?] Bioconductor
## P GenomeInfoDbData 1.2.4 2023-11-28 [?] Bioconductor
## P GenomicRanges 1.46.1 2021-11-18 [?] Bioconductor
## P ggbeeswarm 0.7.1 2022-12-16 [?] CRAN (R 4.1.2)
## P ggforce 0.3.3 2021-03-05 [?] CRAN (R 4.1.2)
## P ggplot2 * 3.4.2 2023-04-03 [?] CRAN (R 4.1.2)
## P ggrastr 1.0.1 2021-12-08 [?] CRAN (R 4.1.2)
## P ggrepel 0.9.1 2021-01-15 [?] CRAN (R 4.1.2)
## P ggridges 0.5.3 2021-01-08 [?] CRAN (R 4.1.2)
## P ggseqlogo 0.1 2017-07-25 [?] CRAN (R 4.1.2)
## P git2r 0.29.0 2021-11-22 [?] CRAN (R 4.1.2)
## P globals 0.14.0 2020-11-22 [?] CRAN (R 4.1.2)
## P glue * 1.6.2 2022-02-24 [?] CRAN (R 4.1.2)
## P goftest 1.2-3 2021-10-07 [?] CRAN (R 4.1.2)
## P gridExtra 2.3 2017-09-09 [?] CRAN (R 4.1.2)
## P gtable 0.3.0 2019-03-25 [?] CRAN (R 4.1.2)
## P here * 1.0.1 2020-12-13 [?] CRAN (R 4.1.2)
## P highr 0.9 2021-04-16 [?] CRAN (R 4.1.2)
## P hms 1.1.1 2021-09-26 [?] CRAN (R 4.1.2)
## P htmltools 0.5.2 2021-08-25 [?] CRAN (R 4.1.2)
## P htmlwidgets 1.5.4 2021-09-08 [?] CRAN (R 4.1.2)
## P httpuv 1.6.5 2022-01-05 [?] CRAN (R 4.1.2)
## P httr 1.4.2 2020-07-20 [?] CRAN (R 4.1.2)
## P ica 1.0-2 2018-05-24 [?] CRAN (R 4.1.2)
## P igraph 2.0.3 2024-03-13 [?] CRAN (R 4.1.2)
## P IRanges 2.28.0 2021-10-26 [?] Bioconductor
## P irlba 2.3.5 2021-12-06 [?] CRAN (R 4.1.2)
## P jquerylib 0.1.4 2021-04-26 [?] CRAN (R 4.1.2)
## P jsonlite 1.8.8 2023-12-04 [?] CRAN (R 4.1.2)
## P KernSmooth 2.23-20 2021-05-03 [?] CRAN (R 4.1.2)
## P knitr 1.37 2021-12-16 [?] CRAN (R 4.1.2)
## P labeling 0.4.2 2020-10-20 [?] CRAN (R 4.1.2)
## P later 1.3.0 2021-08-18 [?] CRAN (R 4.1.2)
## P lattice 0.20-45 2021-09-22 [?] CRAN (R 4.1.2)
## P lazyeval 0.2.2 2019-03-15 [?] CRAN (R 4.1.2)
## P leiden 0.3.9 2021-07-27 [?] CRAN (R 4.1.2)
## P lifecycle 1.0.3 2022-10-07 [?] CRAN (R 4.1.2)
## P listenv 0.8.0 2019-12-05 [?] CRAN (R 4.1.2)
## P lmtest 0.9-39 2021-11-07 [?] CRAN (R 4.1.2)
## P lsa 0.73.2 2020-05-04 [?] CRAN (R 4.1.2)
## P magrittr * 2.0.3 2022-03-30 [?] CRAN (R 4.1.2)
## P MASS 7.3-54 2021-05-03 [?] CRAN (R 4.1.2)
## P Matrix 1.3-4 2021-06-01 [?] CRAN (R 4.1.2)
## P matrixStats 0.61.0 2021-09-17 [?] CRAN (R 4.1.2)
## P memoise 2.0.1 2021-11-26 [?] CRAN (R 4.1.2)
## P mgcv 1.8-38 2021-10-06 [?] CRAN (R 4.1.2)
## P mime 0.12 2021-09-28 [?] CRAN (R 4.1.2)
## P miniUI 0.1.1.1 2018-05-18 [?] CRAN (R 4.1.2)
## P munsell 0.5.0 2018-06-12 [?] CRAN (R 4.1.2)
## P nlme 3.1-153 2021-09-07 [?] CRAN (R 4.1.2)
## P parallelly 1.30.0 2021-12-17 [?] CRAN (R 4.1.2)
## P patchwork 1.1.1 2020-12-17 [?] CRAN (R 4.1.2)
## P pbapply * 1.5-0 2021-09-16 [?] CRAN (R 4.1.2)
## P pillar 1.9.0 2023-03-22 [?] RSPM (R 4.1.2)
## P pkgbuild 1.4.2 2023-06-26 [?] CRAN (R 4.1.2)
## P pkgconfig 2.0.3 2019-09-22 [?] CRAN (R 4.1.2)
## P pkgload 1.3.3 2023-09-22 [?] CRAN (R 4.1.2)
## P plotly 4.10.0 2021-10-09 [?] CRAN (R 4.1.2)
## P plyr 1.8.6 2020-03-03 [?] CRAN (R 4.1.2)
## P png 0.1-7 2013-12-03 [?] CRAN (R 4.1.2)
## P polyclip 1.10-0 2019-03-14 [?] CRAN (R 4.1.2)
## P prettyunits 1.1.1 2020-01-24 [?] CRAN (R 4.1.2)
## P processx 3.8.4 2024-03-16 [?] RSPM
## P profvis 0.3.8 2023-05-02 [?] CRAN (R 4.1.2)
## P promises 1.2.0.1 2021-02-11 [?] CRAN (R 4.1.2)
## P ps 1.7.6 2024-01-18 [?] RSPM
## P purrr * 1.0.1 2023-01-10 [?] CRAN (R 4.1.2)
## P qlcMatrix 0.9.7 2018-04-20 [?] CRAN (R 4.1.2)
## P R6 2.5.1 2021-08-19 [?] CRAN (R 4.1.2)
## P RANN 2.6.1 2019-01-08 [?] CRAN (R 4.1.2)
## P RColorBrewer * 1.1-2 2014-12-07 [?] CRAN (R 4.1.2)
## P Rcpp 1.0.8 2022-01-13 [?] CRAN (R 4.1.2)
## P RcppAnnoy 0.0.19 2021-07-30 [?] CRAN (R 4.1.2)
## P RcppRoll 0.3.0 2018-06-05 [?] CRAN (R 4.1.2)
## P RCurl 1.98-1.5 2021-09-17 [?] CRAN (R 4.1.2)
## P readr * 2.1.1 2021-11-30 [?] CRAN (R 4.1.2)
## P readxl * 1.3.1 2019-03-13 [?] CRAN (R 4.1.2)
## P remotes 2.4.2.1 2023-07-18 [?] CRAN (R 4.1.2)
## P renv 1.0.3 2023-09-19 [?] CRAN (R 4.1.2)
## P reshape2 1.4.4 2020-04-09 [?] CRAN (R 4.1.2)
## P reticulate 1.23 2022-01-14 [?] CRAN (R 4.1.2)
## P rlang 1.1.3 2024-01-10 [?] CRAN (R 4.1.2)
## P rmarkdown 2.11 2021-09-14 [?] CRAN (R 4.1.2)
## P ROCR 1.0-11 2020-05-02 [?] CRAN (R 4.1.2)
## P rpart 4.1-15 2019-04-12 [?] CRAN (R 4.1.2)
## P rprojroot 2.0.2 2020-11-15 [?] CRAN (R 4.1.2)
## P Rsamtools 2.10.0 2021-10-26 [?] Bioconductor
## P Rtsne 0.15 2018-11-10 [?] CRAN (R 4.1.2)
## P S4Vectors 0.32.4 2022-03-24 [?] Bioconductor
## P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.2)
## P scales 1.2.1 2022-08-20 [?] CRAN (R 4.1.2)
## P scattermore 0.7 2020-11-24 [?] CRAN (R 4.1.2)
## P sctransform 0.3.3 2022-01-13 [?] CRAN (R 4.1.2)
## P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.2)
## P Seurat * 4.0.0 2021-01-30 [?] CRAN (R 4.1.2)
## P SeuratObject * 4.0.4 2021-11-23 [?] CRAN (R 4.1.2)
## P shiny 1.7.1 2021-10-02 [?] CRAN (R 4.1.2)
## P Signac * 1.3.0 2021-07-12 [?] CRAN (R 4.1.2)
## P slam 0.1-50 2022-01-08 [?] CRAN (R 4.1.2)
## P SnowballC 0.7.0 2020-04-01 [?] CRAN (R 4.1.2)
## P sparsesvd 0.2 2019-07-15 [?] CRAN (R 4.1.2)
## P spatstat 1.64-1 2020-05-12 [?] CRAN (R 4.1.2)
## P spatstat.data 2.1-2 2021-12-17 [?] CRAN (R 4.1.2)
## P spatstat.utils 2.3-0 2021-12-12 [?] CRAN (R 4.1.2)
## P stringi 1.7.6 2021-11-29 [?] CRAN (R 4.1.2)
## P stringr * 1.5.0 2022-12-02 [?] CRAN (R 4.1.2)
## P survival 3.2-13 2021-08-24 [?] CRAN (R 4.1.2)
## P tensor 1.5 2012-05-05 [?] CRAN (R 4.1.2)
## P tibble 3.2.1 2023-03-20 [?] RSPM (R 4.1.2)
## P tidyr * 1.3.0 2023-01-24 [?] CRAN (R 4.1.2)
## P tidyselect 1.2.0 2022-10-10 [?] CRAN (R 4.1.2)
## P tweenr 1.0.2 2021-03-23 [?] CRAN (R 4.1.2)
## P tzdb 0.3.0 2022-03-28 [?] CRAN (R 4.1.2)
## P urlchecker 1.0.1 2021-11-30 [?] CRAN (R 4.1.2)
## P usethis 2.2.2 2023-07-06 [?] CRAN (R 4.1.2)
## P utf8 1.2.2 2021-07-24 [?] CRAN (R 4.1.2)
## P uwot 0.1.11 2021-12-02 [?] CRAN (R 4.1.2)
## P vctrs 0.6.5 2023-12-01 [?] CRAN (R 4.1.2)
## P vipor 0.4.5 2017-03-22 [?] CRAN (R 4.1.2)
## P viridis * 0.5.1 2018-03-29 [?] RSPM (R 4.1.2)
## P viridisLite * 0.3.0 2018-02-01 [?] CRAN (R 4.1.2)
## P vroom 1.5.7 2021-11-30 [?] CRAN (R 4.1.2)
## P withr 2.5.0 2022-03-03 [?] CRAN (R 4.1.2)
## P xfun 0.29 2021-12-14 [?] CRAN (R 4.1.2)
## P xtable 1.8-4 2019-04-21 [?] CRAN (R 4.1.2)
## P XVector 0.34.0 2021-10-26 [?] Bioconductor
## P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.1.2)
## P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
## P zoo 1.8-9 2021-03-09 [?] CRAN (R 4.1.2)
##
## [1] /project/kleinman/bhavyaa.chandarana/from_hydra/2023-05-NB-FOXR2/public/renv/library/R-4.1/x86_64-pc-linux-gnu
## [2] /home/kleinman/bhavyaa.chandarana/.cache/R/renv/sandbox/R-4.1/x86_64-pc-linux-gnu/145cef2c
##
## P ── Loaded and on-disk path mismatch.
##
## ──────────────────────────────────────────────────────────────────────────────
A project of the Kleinman Lab at McGill University, using the rr reproducible research template.